Best AI tools for< Research Literature >

Jotlify is an AI-powered platform that simplifies complex research papers, making them accessible and easy to understand for students, researchers, professionals, and curious minds. It transforms dense academic content into engaging stories and insights, bridging the gap between complex research and easy understanding. With Jotlify, users can uncover stories and insights that can transform their understanding and impact various aspects of their lives.

Semantic Scholar is a free, AI-powered research tool for scientific literature. It is based at the Allen Institute for AI and provides access to over 217 million papers from all fields of science. Semantic Scholar uses AI to help users discover and explore scientific literature, and to stay up-to-date on the latest research. The tool also includes a number of features to help users manage their research, such as the ability to save papers, create bibliographies, and share research with others.

ResearchBuddy is an AI tool designed to automate the process of conducting literature reviews. It helps researchers and students efficiently gather and analyze information from various sources to enhance their research projects. By leveraging artificial intelligence, ResearchBuddy streamlines the review process, saving users time and effort. The platform offers a user-friendly interface and advanced algorithms to deliver accurate and relevant results. With ResearchBuddy, users can access a comprehensive database of scholarly articles and publications, making it easier to stay up-to-date with the latest research trends and findings.

ScholarAI is an AI-powered scientific research tool that offers a wide range of features to help users navigate and extract insights from scientific literature. With access to over 200 million peer-reviewed articles, ScholarAI allows users to conduct abstract searches, literature mapping, PDF reading, literature reviews, gap analysis, direct Q&A, table and figure extraction, citation management, and project management. The tool is designed to accelerate the research process and provide tailored scientific insights to users.

Open Knowledge Maps is the world's largest AI-based search engine for scientific knowledge. It aims to revolutionize discovery by increasing the visibility of research findings for science and society. The platform is open and nonprofit, based on the principles of open science, with a mission to create an inclusive, sustainable, and equitable infrastructure for all users. Users can map research topics with AI, find documents, and identify concepts to enhance their literature search experience.

Seamless is an AI Literature Review Tool for Scientific Research that enables users to draft literature reviews 100x faster by leveraging advanced AI technology. It allows researchers to find relevant papers and create a draft directly from an excerpt of their work. Seamless utilizes the Semantic Scholar database of scientific papers and large language models like GPT-4 to generate literature reviews in various fields such as engineering, computer science, chemistry, biology, law, medicine, pharma, and business. The tool is designed to streamline the process of literature review creation and enhance the efficiency of researchers and students.

Researcher.Life is a comprehensive research support platform that provides AI-powered tools and expert publication services to empower researchers at every stage of their journey. With a suite of advanced AI tools, including Paperpal, R Discovery, and Mind the Graph, Researcher.Life helps researchers write better, discover relevant literature, create stunning scientific illustrations, and find the right journals for their work. Additionally, Researcher.Life offers expert publication services from Editage, ensuring that manuscripts are polished and ready for publication. By combining AI technology with human expertise, Researcher.Life simplifies complex research tasks, saves time, and accelerates the path to success for researchers worldwide.

Jenni is an AI-powered text editor that helps you write, edit, and cite with confidence. It offers a range of features to enhance your research and writing capabilities, including autocomplete, in-text citations, paraphrasing, and a reference library. Trusted by universities and businesses worldwide, Jenni has helped over 3 million academics write over 970 million words.

Bionl is a no-code bioinformatics platform designed to streamline biomedical research for researchers and scientists. It offers a full workspace with features such as bioinformatics pipelines customization, GenAI for data analysis, AI-powered literature search, PDF analysis, and access to public datasets. Bionl aims to automate cloud, file system, data, and workflow management for efficient and precise analyses. The platform caters to Pharma and Biotech companies, academic researchers, and bioinformatics CROs, providing powerful tools for genetic analysis and speeding up research processes.

SciSpace is an AI-powered tool that helps researchers understand research papers better. It can explain and elaborate most academic texts in simple words. It is a great tool for students, researchers, and anyone who wants to learn more about a particular topic. SciSpace has a user-friendly interface and is easy to use. Simply upload a research paper or enter a URL, and SciSpace will do the rest. It will highlight key concepts, provide definitions, and generate a summary of the paper. SciSpace can also be used to generate citations and find related papers.

DrugCard is an AI-enabled Data Intelligence platform designed to streamline drug safety routines for pharmacovigilance processes. It offers solutions for local literature screening, catering to CROs, MAHs, and freelancers in the pharmaceutical industry. With support for multiple languages and regions, DrugCard ensures continuous, transparent, and scalable drug safety processes, saving time and improving efficiency. The platform leverages AI technology to enhance pharmacovigilance practices, providing accurate and holistic screening of medical journals to meet regulatory requirements.

OpenRead is an AI-powered research tool that helps users discover, understand, and organize scientific literature. It offers a variety of features to make research more efficient and effective, including semantic search, AI summarization, and note-taking tools. OpenRead is designed to help researchers of all levels, from students to experienced professionals, save time and improve their research outcomes.

Cambrian Copilot is an AI tool designed for researchers and engineers to easily stay up-to-date with the latest machine learning research. With over 240,000 ML papers available for search, the tool helps users understand complex details and automate literature reviews, simplifying the process of discovering and accessing cutting-edge research in the field of machine learning.

Insight is an AI-powered medical research tool that serves as a research assistant for generating scientific summaries, hypotheses, experimental designs, and target identification. It empowers scientists to navigate literature, formulate hypotheses, and design experiments by utilizing peer-reviewed databases to provide reliable outputs. With integrated features like NIH PubMed access, NIH Reporter insights, and MYGENE & MYVARIANT deep dives, Insight streamlines the research process and accelerates discoveries in the medical field.

Afforai is a powerful AI research assistant and chatbot that serves as an AI-powered reference manager for researchers. It helps manage, annotate, cite papers, and conduct literature reviews with AI reliably. With features like managing research papers, annotating and highlighting notes, managing citations and metadata, collaborating on notes, and supporting various document formats, Afforai streamlines academic workflows and enhances research productivity. Trusted by over 50,000 researchers worldwide, Afforai offers advanced AI capabilities, including GPT-4 and Claude 3.5 Sonnet, along with secure data handling and seamless integrations.

Epsilon is an AI search engine designed for scientific research solutions. It helps researchers find evidence, citations, and relevant information from over 200 million academic papers. Epsilon can summarize passages, group search results, extract key information from multiple papers, and provide comprehensive summaries. Trusted by over 30,000 researchers worldwide, Epsilon is a reliable tool for conducting literature reviews, drafting proposals, and executing research projects.

Emdash is an AI-powered tool designed to help users organize their book highlights effectively. By utilizing AI technology, Emdash can analyze and categorize text snippets, making it easier for users to remember and learn from their readings. The tool offers features such as conceptual cousins, instant semantic search, tagging, rating, note-taking, and reflection capabilities. Users can also export their organized data back to epub format for review on e-readers. Emdash is free, open-source, and aims to provide a seamless reading experience for book enthusiasts.

Rayyan is an intelligent systematic review tool trusted by over 500,000 researchers worldwide. It helps users organize, manage, and accelerate collaborative systematic literature reviews. Rayyan empowers users to work remotely and collaborate with distributed research teams, offering membership packages with onboarding, training, and priority support. The tool is designed to understand language, learn from user decisions, and facilitate quick navigation through systematic reviews. Rayyan also provides solutions for organizations and businesses to streamline research processes and save valuable researcher time.

SciSummary is an AI-powered tool designed to summarize scientific articles and research papers quickly and efficiently. It leverages cutting-edge Artificial Intelligence models like GPT-3.5 and GPT-4 to provide accurate and concise summaries for busy scientists, students, and enthusiasts. With features such as unlimited summaries, figure and table analysis, and easy document import, SciSummary aims to streamline the process of digesting complex scientific content. The tool is widely used by researchers, students, and faculty across major universities in the US, offering a valuable solution for literature review, research trends tracking, and information retrieval.

Nature is a scientific journal that publishes original research, reviews, news, and commentary on a wide range of scientific disciplines. It is one of the world's most prestigious scientific journals, and its articles are widely cited in the scientific literature. Nature is published by Springer Nature, a leading global publisher of scientific, technical, and medical content.

Jarvis is an AI note-taking assistant for Joplin, powered by online and offline LLMs (such as OpenAI's ChatGPT or GPT-4, Hugging Face, Google PaLM, Universal Sentence Encoder). You can chat with it (including prompt templates), use your personal notes as additional context in the chat, automatically annotate notes, perform semantic search, or compile an automatic review of the scientific literature.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

WritingAIPaper is a comprehensive guide for beginners on crafting AI conference papers. It covers topics like paper structure, core ideas, framework construction, result analysis, and introduction writing. The guide aims to help novices navigate the complexities of academic writing and contribute to the field with clarity and confidence. It also provides tips on readability improvement, logical strength, defensibility, confusion time reduction, and information density increase. The appendix includes sections on AI paper production, a checklist for final hours, common negative review comments, and advice on dealing with paper rejection.

A curated list of Generative AI projects, tools, artworks, and models

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

This repository provides a comprehensive collection of AI tools and resources to enhance literature reviews. It includes a curated list of AI tools for various tasks, such as identifying research gaps, discovering relevant papers, visualizing paper content, and summarizing text. Additionally, the repository offers materials on generative AI, effective prompts, copywriting, image creation, and showcases of AI capabilities. By leveraging these tools and resources, researchers can streamline their literature review process, gain deeper insights from scholarly literature, and improve the quality of their research outputs.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

This repository is a collection of papers and resources related to recommendation systems, focusing on foundation models, transferable recommender systems, large language models, and multimodal recommender systems. It explores questions such as the necessity of ID embeddings, the shift from matching to generating paradigms, and the future of multimodal recommender systems. The papers cover various aspects of recommendation systems, including pretraining, user representation, dataset benchmarks, and evaluation methods. The repository aims to provide insights and advancements in the field of recommendation systems through literature reviews, surveys, and empirical studies.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

GPT Academic is a powerful tool that leverages the capabilities of large language models (LLMs) to enhance academic research and writing. It provides a user-friendly interface that allows researchers, students, and professionals to interact with LLMs and utilize their abilities for various academic tasks. With GPT Academic, users can access a wide range of features and functionalities, including: * **Summarization and Paraphrasing:** GPT Academic can summarize complex texts, articles, and research papers into concise and informative summaries. It can also paraphrase text to improve clarity and readability. * **Question Answering:** Users can ask GPT Academic questions related to their research or studies, and the tool will provide comprehensive and well-informed answers based on its knowledge and understanding of the relevant literature. * **Code Generation and Explanation:** GPT Academic can generate code snippets and provide explanations for complex coding concepts. It can also help debug code and suggest improvements. * **Translation:** GPT Academic supports translation of text between multiple languages, making it a valuable tool for researchers working with international collaborations or accessing resources in different languages. * **Citation and Reference Management:** GPT Academic can help users manage their citations and references by automatically generating citations in various formats and providing suggestions for relevant references based on the user's research topic. * **Collaboration and Note-Taking:** GPT Academic allows users to collaborate on projects and take notes within the tool. They can share their work with others and access a shared workspace for real-time collaboration. * **Customizable Interface:** GPT Academic offers a customizable interface that allows users to tailor the tool to their specific needs and preferences. They can choose from a variety of themes, adjust the layout, and add or remove features to create a personalized workspace. Overall, GPT Academic is a versatile and powerful tool that can significantly enhance the productivity and efficiency of academic research and writing. It empowers users to leverage the capabilities of LLMs and unlock new possibilities for academic exploration and knowledge creation.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.

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I can assist doctors with information synthesis, medical literature reviews, patient education material, diagnostic guidelines, treatment options, ethical dilemmas, and staying updated on medical research and innovations.

Rapid, precise medical research expert in orthopaedics.

Bilingual expert in modern Persian literature and poetry

你说上联，我说下联 I compose the second half of Chinese couplets in response to user prompts.

I translate books into various languages, focusing on specific chapters.

Confucian scholar reading Chinese poems with explanations and Q&A

Engaging, friendly guide for academic literature reviews using research question or topic

SGF identifies research gaps using scholarly sources. It creates proposals with abstracts, literature reviews, and a reference list tailored for academic research.

An Ivy Leage Scholar GPT equipped to understand your research needs, formulate comprehensive literature review strategies, and extract pertinent information from a plethora of academic databases and journals. I'll then compose a peer review-quality paper with citations.

Writes main argument in referenced academic paragraphs.

for relevant literature review and recent

I assist scientists with literature and social media.

Summarizes academic papers, explains figures and tables.

Analytical and encouraging literature review assistant.

Chemical biology expert for literature, data viz, and coding

Confident advisor for literature search, focusing on broad contexts.

This GPT has access to both PubMed and the UnPaywall database, allowing conversational exploration of the literature and direct access to full-text articles

Aging biology expert blending baseline knowledge with user literature

Upon uploading a research paper, I provide a concise section wise analysis covering Abstract, Lit Review, Findings, Methodology, and Conclusion. I also critique the work, highlight its strengths, and answer any open questions from my Knowledge base of Open source materials.